Software Engineering Intern, Data & Machine Learning

About the role

You’ll join the data and ML engineering track with a dedicated mentor who works across data engineering and applied ML — weekly 1:1s, pipeline reviews, and structured ramp milestones.

Mentorship is how we help you get there — not a reason to lower it.

You’ll collaborate directly with the .NET team on data contracts between systems — the work does not exist in isolation.

We expect you to be 3 days on-site in Glendale, with flexibility around your academic schedule.

Fully remote is not offered.

Qualifications

• Solid Python — functions, classes, error handling, and code that someone else can read.
• Data manipulation with pandas, polars, or equivalent — load a dataset, clean it, answer questions from it without fighting the tools.
• SQL — non-trivial queries and a real understanding of what a join is doing.
• AI tool usage that is habitual and specific: you’ve used LLMs to accelerate EDA, write boilerplate, or debug data issues, and you can describe exactly how. This is evaluated explicitly.
• Genuine intellectual curiosity about data — you want to know why a number looks wrong, not just make the error go away.

Nice to Have

• ML library exposure: scikit-learn, PyTorch, or similar. You don’t need production model experience, but you should know what a train/test split is and why it matters.
• Data pipeline tooling: Airflow, Prefect, dbt, or similar.
• LangChain, OpenAI/Anthropic API integration, or agent workflow experience.
• Cloud data services on Azure, AWS, or GCP.
• FastAPI or Python-based API experience.
• Statistics coursework — not required, but genuinely useful for the ML work.

What You’ll Get

• Competitive hourly compensation, tiered by experience (undergraduate and graduate rates; details shared during the process).
• A dedicated mentor working across data engineering and applied ML — enough runway to see features go from prototype to production over a 6–12 month engagement.
• Work that ships — features you build will go to production users during the internship.
• Real code review under the same standards applied to the full-time team — not the kind that approves everything.
• AI tooling stipend (Cursor Pro, Claude Pro, or equivalent) — the AI-native expectation is real; we remove the financial barrier to getting there.
• Priority consideration for full-time roles upon graduation.
• Access to real-world home services operational data — the problems are genuine, not synthetic.

Location & Hybrid Policy

• This role is based in Glendale, CA.
• We expect 3 days on-site per week, with flexibility around academic schedules communicated in advance.
• Fully remote arrangements are not offered.
• Candidates who cannot commit to regular on-site presence in Glendale are not a fit for this program.

How to Apply

Send your resume. A notebook, a project, or any data work you can share is ideal — include a link and a brief note on what you built and why. No shareable work? Describe the most interesting data problem you’ve tackled: the question, your approach, and what you found.

Applications are reviewed on a rolling basis. We recruit year-round for this track.

Moon is committed to building a diverse and inclusive team. We encourage applications from candidates of all backgrounds, institutions, and experience levels. We evaluate based on demonstrated ability, not credentials.

The pay range for this role is:

• 25 - 35 USD per hour (Moon HQ)

Engineering

Glendale, CA